Defect detection of industrial objects has become a common demand nowadays. For example, car manufactures need to make sure there is no exterior defect on the cars before they are put into the market. Such demand also exists for microscopic objects, for example, the exterior defect detection for LED panels, smart phones and so on. Generally, there could be various types of defects that may occur on an object. For example, there could be more than 100 types of defects on a LED panel's surface.
A traditional way to detect the defects of objects is manual inspection, which means labors need to check each object using unaided eyes or magnifiers to identify the defects. However, this is quite a time-consuming work which needs certain amount of hired labors. Further, manual inspection is very slow and prone to be erroneous after long time work. There could be potentially 200 million volume of images that need manual inspections to be performed every day in a cell phone factory. Especially when the objects to be inspected are small or even tiny, it is almost unlikely for human beings to do the manual inspection. Thus, there is a need for automatic detection of defects for industrial objects to relieve human beings from the manual inspection to improve the efficiency and reduce errors and cost.